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aaai_hydra

Introduction

This repository contains the implementation code of HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks.

HYDRA is a method of neural network interpretability that assesses the contribution of training data. You can play this demo to get a feel for its power.

Installation

Dependency

Supported Operating Systems

Linux and Windows should work for recent versions of PyTorch.

Software dependency

PyTorch >= 2.1
A C++20 compiler

Steps to install

Here it's assumed that pip is used as the package manager.

  1. Install PyTorch
pip3 install torch --user
  1. Install a PyTorch extension for storing tensors.
git clone --recursive [email protected]:cyyever/torch_cpp_extension.git
cd torch_cpp_extension
mkdir build && cd build
cmake -DBUILD_SHARED_LIBS=on -DBUILD_TESTING=off ..
cmake --build . --config release
cd ..
env cmake_build_dir=build python3 setup.py install --user
  1. Install the dependent libraries.
pip3 install -r requirements.txt --user

Citation

If you find our work useful, please cite it:

@article{chen2021hydra,
  title={Hydra: Hypergradient data relevance analysis for interpreting deep neural networks},
  author={Chen, Yuanyuan and Li, Boyang and Yu, Han and Wu, Pengcheng and Miao, Chunyan},
  journal={arXiv preprint arXiv:2102.02515},
  year={2021}
}

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